chr7.22128_chr7_128964725_128967485_+_1.R 

fitVsDatCorrelation=0.860049258497641
cont.fitVsDatCorrelation=0.269237305567358

fstatistic=9499.66351134137,43,485
cont.fstatistic=2658.06165457934,43,485

residuals=-0.433729450911217,-0.0814882888956674,-0.00220021269000107,0.08501900899331,0.574293405024197
cont.residuals=-0.64182910422708,-0.209015962346687,-0.0203810858686647,0.163714058208325,1.00968732087261

predictedValues:
Include	Exclude	Both
chr7.22128_chr7_128964725_128967485_+_1.R.tl.Lung	74.1076963347323	50.530002386397	73.9738369257437
chr7.22128_chr7_128964725_128967485_+_1.R.tl.cerebhem	88.835048478989	63.5209737394103	71.3875518529171
chr7.22128_chr7_128964725_128967485_+_1.R.tl.cortex	89.7622764865604	49.1535867656145	113.922471888647
chr7.22128_chr7_128964725_128967485_+_1.R.tl.heart	79.4572260762648	48.0076860883197	86.4947433896241
chr7.22128_chr7_128964725_128967485_+_1.R.tl.kidney	74.4885556688218	50.5620419842121	74.5856696476685
chr7.22128_chr7_128964725_128967485_+_1.R.tl.liver	76.7806892019123	52.5875038114039	69.35611614258
chr7.22128_chr7_128964725_128967485_+_1.R.tl.stomach	73.4026778194833	52.9871977424056	70.5157994389852
chr7.22128_chr7_128964725_128967485_+_1.R.tl.testicle	81.3132344868635	54.9083353077135	73.5044277456695


diffExp=23.5776939483353,25.3140747395787,40.6086897209458,31.4495399879452,23.9265136846097,24.1931853905084,20.4154800770777,26.4048991791500
diffExpScore=0.995389369513418
diffExp1.5=0,0,1,1,0,0,0,0
diffExp1.5Score=0.666666666666667
diffExp1.4=1,0,1,1,1,1,0,1
diffExp1.4Score=0.857142857142857
diffExp1.3=1,1,1,1,1,1,1,1
diffExp1.3Score=0.888888888888889
diffExp1.2=1,1,1,1,1,1,1,1
diffExp1.2Score=0.888888888888889

cont.predictedValues:
Include	Exclude	Both
Lung	70.268925111752	68.5491408196159	65.049532821237
cerebhem	77.6812482420228	69.5934611852623	72.398314144776
cortex	70.905951318415	70.1361026906682	65.123569443012
heart	67.3194416156174	63.7651185114689	71.169910306671
kidney	66.7031941941051	67.3172669961085	74.751779442856
liver	69.433030504725	60.1096198090796	63.4406416478857
stomach	70.1709022511336	71.5726401348359	67.9519136280883
testicle	73.4311808399453	65.7629617566379	73.2638059974797
cont.diffExp=1.71978429213615,8.08778705676058,0.769848627746853,3.55432310414845,-0.614072802003378,9.32341069564533,-1.40173788370230,7.66821908330745
cont.diffExpScore=1.10069302037431

cont.diffExp1.5=0,0,0,0,0,0,0,0
cont.diffExp1.5Score=0
cont.diffExp1.4=0,0,0,0,0,0,0,0
cont.diffExp1.4Score=0
cont.diffExp1.3=0,0,0,0,0,0,0,0
cont.diffExp1.3Score=0
cont.diffExp1.2=0,0,0,0,0,0,0,0
cont.diffExp1.2Score=0

tran.correlation=0.405853721655763
cont.tran.correlation=0.33216369273063

tran.covariance=0.00265020993008894
cont.tran.covariance=0.000925682838807424

tran.mean=66.275295773694
cont.tran.mean=68.9200116238371

weightedLogRatios:
wLogRatio
Lung	1.57548163323043
cerebhem	1.44866600093709
cortex	2.52692858486924
heart	2.07755146953375
kidney	1.59507872824266
liver	1.57131917781095
stomach	1.34698813373224
testicle	1.64988339883317

cont.weightedLogRatios:
wLogRatio
Lung	0.105060616041508
cerebhem	0.472496878937008
cortex	0.0464603025723842
heart	0.226861021346059
kidney	-0.0385328141396312
liver	0.601034013451306
stomach	-0.084275474584161
testicle	0.467770153173509

varWeightedLogRatios=0.150731830663672
cont.varWeightedLogRatios=0.067565516682526

coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.07354877523816	0.0764109232405085	53.3110791295689	6.0984822697264e-205	***
df.mm.trans1	0.287158145665284	0.0611709400048152	4.69435561465427	3.48541868199251e-06	***
df.mm.trans2	-0.153625442068026	0.0611709400048152	-2.51141215184748	0.0123488482537743	*  
df.mm.exp2	0.445652716355243	0.0819120560483095	5.44062422377006	8.43816686193345e-08	***
df.mm.exp3	-0.267778704650877	0.0819120560483095	-3.26910002714312	0.00115554978591481	** 
df.mm.exp4	-0.137878858984922	0.0819120560483094	-1.68325476903675	0.0929693024397106	.  
df.mm.exp5	-0.00247694008504696	0.0819120560483095	-0.0302390173625495	0.975888870582847	   
df.mm.exp6	0.139802170005483	0.0819120560483095	1.7067349636912	0.088511417516247	.  
df.mm.exp7	0.0857987776502819	0.0819120560483095	1.04744993337342	0.295413820034571	   
df.mm.exp8	0.182253125394584	0.0819120560483095	2.22498535853008	0.0265414702303542	*  
df.mm.trans1:exp2	-0.264390845223014	0.064257103413667	-4.11457770701161	4.55737785925154e-05	***
df.mm.trans2:exp2	-0.216849836414344	0.0642571034136671	-3.37472162444567	0.00079815802370536	***
df.mm.trans1:exp3	0.459424116955616	0.0642571034136671	7.1497794414726	3.21781529835914e-12	***
df.mm.trans2:exp3	0.240161257952929	0.0642571034136671	3.73750519700283	0.000207999443346651	***
df.mm.trans1:exp4	0.207578307999980	0.064257103413667	3.23043363258465	0.00131996602420297	** 
df.mm.trans2:exp4	0.0866727173827246	0.064257103413667	1.34884258359349	0.178016970624723	   
df.mm.trans1:exp5	0.00760304741197991	0.0642571034136671	0.118322286689985	0.905861287189835	   
df.mm.trans2:exp5	0.00311080992292179	0.0642571034136671	0.0484119226927391	0.96140788653491	   
df.mm.trans1:exp6	-0.104368395227261	0.0642571034136671	-1.62423124732795	0.104976145923210	   
df.mm.trans2:exp6	-0.0998909151426198	0.064257103413667	-1.5545505451678	0.120705420071620	   
df.mm.trans1:exp7	-0.095357751215976	0.0642571034136671	-1.48400326423201	0.138457628899055	   
df.mm.trans2:exp7	-0.03831571183778	0.0642571034136671	-0.596287566700843	0.551261331977319	   
df.mm.trans1:exp8	-0.0894637273476727	0.0642571034136671	-1.39227762527254	0.164476367398214	   
df.mm.trans2:exp8	-0.0991552278362094	0.0642571034136671	-1.54310142487873	0.123458367977121	   
df.mm.trans1:probe2	-0.00876678963114505	0.0439938312745096	-0.199273156648773	0.842132590271787	   
df.mm.trans1:probe3	-0.385261390752889	0.0439938312745096	-8.75716843911507	3.35397543485476e-17	***
df.mm.trans1:probe4	-0.185967636647458	0.0439938312745096	-4.2271298329775	2.82913229403362e-05	***
df.mm.trans1:probe5	-0.0630235036858895	0.0439938312745096	-1.43255319802997	0.152629772853709	   
df.mm.trans1:probe6	-0.23998115653839	0.0439938312745096	-5.45488195926771	7.82447728205414e-08	***
df.mm.trans2:probe2	0.0933456716454259	0.0439938312745096	2.12179000876223	0.0343617618410187	*  
df.mm.trans2:probe3	-0.087794180714332	0.0439938312745096	-1.99560206899281	0.0465361009507167	*  
df.mm.trans2:probe4	0.0145001463088835	0.0439938312745096	0.32959498840659	0.741848306683946	   
df.mm.trans2:probe5	0.0518844562589831	0.0439938312745096	1.17935753163297	0.238834034034126	   
df.mm.trans2:probe6	-0.0296331597227819	0.0439938312745096	-0.673575336912102	0.500902196739712	   
df.mm.trans3:probe2	0.287910722255165	0.0439938312745096	6.54434301160724	1.5223955976921e-10	***
df.mm.trans3:probe3	0.346530163726514	0.0439938312745096	7.87678985183762	2.22230428226581e-14	***
df.mm.trans3:probe4	0.103712943212869	0.0439938312745096	2.35744285524323	0.0187975933279114	*  
df.mm.trans3:probe5	-0.0574011607885614	0.0439938312745096	-1.30475476051162	0.192595112816988	   
df.mm.trans3:probe6	-0.133790498895169	0.0439938312745096	-3.04111951651478	0.0024845861749075	** 
df.mm.trans3:probe7	0.0325901260924652	0.0439938312745096	0.740788541218692	0.459180154877563	   
df.mm.trans3:probe8	0.373840108146245	0.0439938312745096	8.4975574373958	2.39793782644294e-16	***
df.mm.trans3:probe9	-0.0219633544589907	0.0439938312745096	-0.499237138996722	0.617838709655478	   
df.mm.trans3:probe10	0.0348709186685429	0.0439938312745096	0.792632004495307	0.428379801265255	   

cont.coeff:
Name	Estimate	Std-Error	t-value	Pr(>|t|)	Signif
df.mm.(Intercept)	4.4543316625719	0.144233193979522	30.8828470040282	1.34455436638877e-116	***
df.mm.trans1	-0.183869269909183	0.115466214533930	-1.59240753367862	0.111944668513057	   
df.mm.trans2	-0.242643155028675	0.115466214533930	-2.10142123397813	0.0361193215853391	*  
df.mm.exp2	0.0083699757438254	0.154617127607405	0.0541335612253642	0.956851044314891	   
df.mm.exp3	0.0307739968210984	0.154617127607405	0.199033556613715	0.842319905533276	   
df.mm.exp4	-0.205146254939099	0.154617127607405	-1.3268016170886	0.185198651611409	   
df.mm.exp5	-0.209234922957385	0.154617127607405	-1.35324544049649	0.176607629371241	   
df.mm.exp6	-0.118303619001517	0.154617127607405	-0.765139159109898	0.44456083544527	   
df.mm.exp7	-0.00188521307222903	0.154617127607405	-0.0121927829174000	0.990276821529847	   
df.mm.exp8	-0.116392784556658	0.154617127607405	-0.752780667690299	0.451946945598211	   
df.mm.trans1:exp2	0.0919142485955353	0.121291653970127	0.757795327106128	0.448941553268065	   
df.mm.trans2:exp2	0.00674976639642057	0.121291653970127	0.0556490589046052	0.95564429847882	   
df.mm.trans1:exp3	-0.0217492957364909	0.121291653970127	-0.179314033773896	0.857766013984246	   
df.mm.trans2:exp3	-0.00788719067008554	0.121291653970127	-0.0650266560964542	0.948179570297557	   
df.mm.trans1:exp4	0.162265661205794	0.121291653970127	1.33781390470410	0.181584054511292	   
df.mm.trans2:exp4	0.132801691683278	0.121291653970127	1.09489554587149	0.274105832357357	   
df.mm.trans1:exp5	0.157158095157742	0.121291653970127	1.29570411494635	0.19569375290797	   
df.mm.trans2:exp5	0.191100821951063	0.121291653970127	1.57554799275909	0.115782044369390	   
df.mm.trans1:exp6	0.106336648619359	0.121291653970127	0.876702107183314	0.381082489984778	   
df.mm.trans2:exp6	-0.0130773611861494	0.121291653970127	-0.10781748585414	0.914185059173669	   
df.mm.trans1:exp7	0.000489271787242204	0.121291653970127	0.00403384545619856	0.996783124344915	   
df.mm.trans2:exp7	0.0450472206910602	0.121291653970127	0.371395881056706	0.710504879710276	   
df.mm.trans1:exp8	0.160411768514194	0.121291653970127	1.32252931890683	0.186615243579247	   
df.mm.trans2:exp8	0.0748987008695945	0.121291653970127	0.617509106504901	0.537188789272441	   
df.mm.trans1:probe2	-0.0563286214075572	0.0830427186456867	-0.678308975503212	0.497899308189182	   
df.mm.trans1:probe3	-0.108096967096348	0.0830427186456867	-1.30170313375166	0.193635820540210	   
df.mm.trans1:probe4	-0.0685338142774876	0.0830427186456867	-0.825283846617506	0.409616195551087	   
df.mm.trans1:probe5	-0.0158333699642478	0.0830427186456867	-0.190665361424439	0.84886756107354	   
df.mm.trans1:probe6	-0.0413308131910801	0.0830427186456867	-0.497705444440274	0.618917247948919	   
df.mm.trans2:probe2	0.108439100217734	0.0830427186456867	1.30582309907753	0.192231749563659	   
df.mm.trans2:probe3	0.0368194807479086	0.0830427186456867	0.443380001863909	0.657688612557841	   
df.mm.trans2:probe4	-0.0150392692242460	0.0830427186456867	-0.181102804309830	0.85636253924945	   
df.mm.trans2:probe5	0.0844551703003818	0.0830427186456867	1.01700873571735	0.309656329957545	   
df.mm.trans2:probe6	0.0391233540518957	0.0830427186456867	0.471123232595755	0.637764569507164	   
df.mm.trans3:probe2	0.129639290074524	0.0830427186456867	1.56111567863822	0.119148706849404	   
df.mm.trans3:probe3	0.111176548487509	0.0830427186456867	1.33878743736533	0.181267052956117	   
df.mm.trans3:probe4	0.212102581745597	0.0830427186456867	2.55413822192602	0.0109496655935868	*  
df.mm.trans3:probe5	0.194986838387416	0.0830427186456867	2.34803052654567	0.0192737757157628	*  
df.mm.trans3:probe6	0.222256081320646	0.0830427186456867	2.67640661270897	0.00769356447926081	** 
df.mm.trans3:probe7	0.149311935719384	0.0830427186456867	1.79801357848657	0.0727966371530995	.  
df.mm.trans3:probe8	0.236547199415073	0.0830427186456867	2.84850018487875	0.00457922138390978	** 
df.mm.trans3:probe9	0.136274718489487	0.0830427186456867	1.64101947421691	0.101441665703379	   
df.mm.trans3:probe10	0.0810026609362636	0.0830427186456867	0.975433635330181	0.329831466183652